I nā makahiki ʻelima i hala iho nei, ua lilo ka ʻepekema data i ka huaʻōlelo ʻenehana helu ʻekahi. ʻO kēia no ka mea ke hoʻohua nei nā hui i nā ʻikepili hou aʻe i kēlā me kēia lā, ʻoiai me ka hiki ʻana mai o ka Internet of Things (IoT).
Ke ʻimi nei nā ʻoihana i nā ʻepekema ʻikepili hiki ke kōkua iā lākou e hoʻomaopopo i kēia ʻikepili āpau. Inā ʻoe e ʻimi nei i kahi e loaʻa ai ke kiʻekiʻe ʻepekema ʻikepili maikaʻi loa, a laila pono ʻoe e hoʻomau i ka heluhelu ʻana i kēia ʻatikala ma ka Best Data Science Colleges in the World.
No laila, ua hōʻike ʻia kahi hōʻike a IBM e loaʻa ana he 2.7 miliona mau wehe hana ma ka ʻepekema data a me ka analytics e 2025. E uku ʻia nā ʻepekema data ma kahi o $35 biliona i kēlā me kēia makahiki ma US wale nō.
He waiwai nui ka hana, ʻaʻole ʻo ka poʻe ʻoihana wale nō ka mea e hoʻāʻo nei i ko lākou lima akā ʻo nā haumāna pū kekahi i hoʻopau i kā lākou puka. Inā he haumāna ʻoe, e noʻonoʻo paha ʻoe i ke kulanui āu e koho ai inā makemake ʻoe i kahi ʻoihana ma ka ʻepekema data?
Eia nō naʻe, no ka pane ʻana i kēia nīnau, ua hōʻuluʻulu mākou i kahi papa inoa o nā koleke e hāʻawi ana i nā papa maikaʻi loa i ka ʻepekema Data. Ua helu ʻia kēia mau koleke ma muli o nā kumu e like me ka helu Placement, Quality of faculty, Infrastructure facilities, a me ka ʻoihana alumni.
Ua nānā pū mākou i nā ʻoihana i ka ʻepekema data a me nā mea ʻē aʻe āu e ʻike ai e pili ana i ka ʻepekema data a me nā koleke ʻepekema data.
Table of Contents
He aha ka ʻepekema ʻikepili?
ʻO ka ʻepekema ʻikepili kahi kahua noiʻi e pili ana i ka hoʻoili ʻana i ka nui o ka ʻikepili. ʻO ia ka ʻoihana ulu wikiwiki loa i ka ʻenehana no ʻehā makahiki i ka lālani, a ʻo ia kekahi o nā hana uku kiʻekiʻe loa.
ʻO kahi ʻoihana i ka ʻepekema data kahi koho maikaʻi loa no ka poʻe e ʻimi nei e hana i ka hopena i kā lākou hana.
ʻO nā ʻepekema ʻikepili he poʻe loea e hiki ke hōʻiliʻili, mālama, hana, kālailai, ʻike a wehewehe i ka nui o ka ʻike me ka hoʻohana ʻana i nā ʻenehana a me nā lako polokalamu. Lawe lākou i nā hopena koʻikoʻi mai ka ʻikepili paʻakikī a haʻi maopopo i kā lākou hopena i nā poʻe ʻē aʻe.
ʻO nā ʻepekema ʻikepili he poʻe loea i aʻo ʻia i ka helu, ke aʻo ʻana i nā mīkini, nā ʻōlelo papahana e like me Python a me R, a me nā mea hou aku. He poʻe akamai lākou i ka unuhi ʻana i nā ʻike e kōkua i nā hui e hana i nā hoʻoholo ʻoihana maikaʻi aʻe i hiki iā lākou ke ulu wikiwiki a ʻoi aku ka maikaʻi.
ʻO ka hapa maikaʻi loa? Maikaʻi ka uku - ʻo ka uku maʻamau o kahi ʻepekema data he $117,345 i kēlā me kēia makahiki e like me Glassdoor.
He aha ka hana a nā ʻepekema ʻikepili?
He kahua hou ka ʻepekema ʻikepili, akā ua pahū ia i ka hapalua o nā makahiki i hala. Ke ulu nui nei ka nui o ka ʻikepili a mākou e hana ai i kēlā me kēia makahiki, a ʻo kēia kahe o ka ʻike e hana i nā manawa hou no nā ʻoihana a me nā kānaka.
ʻO ka ʻepekema ʻikepili kahi hui ʻana o nā mea hana like ʻole, algorithms, a me nā loina aʻo mīkini e ʻike i nā kumu huna mai ka ʻikepili maka.
He kahua multidisciplinary ia e hoʻohana ana i nā ʻano ʻepekema, nā kaʻina hana, nā algorithms, a me nā ʻōnaehana e unuhi i ka ʻike a me nā ʻike mai ka nui o nā ʻikepili i kūkulu ʻia a i kūkulu ʻole ʻia. Pili ka ʻepekema ʻikepili i ka mining data, ke aʻo ʻana i ka mīkini, a me ka ʻikepili nui.
ʻO kahi ʻoihana i ka ʻepekema data e ʻae iā ʻoe e hoʻoponopono i kekahi o nā pilikia paʻakikī me ka hoʻohana ʻana i kāu mau mākau analytical. ʻO ke kuleana o ka ʻepekema data ʻo ka hoʻohuli ʻana i ka ʻikepili maka i mau ʻike hiki ke hana.
Eia kekahi mau hana maʻamau:
- E ʻike i nā kumu ʻikepili koʻikoʻi a e hoʻokaʻawale i nā kaʻina ohi
- E hana mua i ka ʻikepili i kūkulu ʻia a i hoʻonohonoho ʻole ʻia
- E noʻonoʻo i ka nui o ka ʻike e ʻike i nā ʻano a me nā ʻano
- E kūkulu i nā hiʻohiʻona wānana a me nā algorithm aʻo mīkini
- Hoʻohui i nā hiʻohiʻona ma o ka hoʻohālike ensemble
- Hōʻike i ka ʻike me ka hoʻohana ʻana i nā ʻenehana hōʻike ʻike.
No ke aha ʻEpekema ʻIkepili?
Hoʻohana ʻia nā ʻepekema ʻikepili e nā hui mai nā ʻoihana like ʻole, a hana lākou ma nā ʻano papahana like ʻole. Ke piʻi nui nei ka noi no nā ʻepekema Data i kēlā me kēia lā, no ke aha? ʻO ka ʻepekema ʻikepili kekahi o nā hana wela loa i ka ʻenehana, a ʻo ka pono o nā ʻepekema data e manaʻo ʻia e ulu e 30 pakeneka mai 2019 a 2025, e like me ka IBM.
Ke ulu wikiwiki nei ke kahua o ka ʻepekema data no laila ʻaʻole lawa ka poʻe akamai kūpono e hoʻopiha i nā kūlana wehe. Loaʻa ka nele o ka poʻe me nā mākau i koi ʻia, me ka ʻike o ka makemakika, nā helu, nā polokalamu a me ka ʻoihana acumen. A ma muli o kona paʻakikī a me ka ʻokoʻa, nui nā ʻoihana e hakakā nei me ka hoʻolimalima ʻana i nā ʻepekema data.
Akā no ke aha e mālama nui ai nā ʻoihana i ka ʻepekema data? He maʻalahi ka pane: Hiki i ka ʻikepili ke kōkua i ka hoʻololi ʻana i kahi ʻoihana i kahi hui agile e hoʻololi koke i ka loli.
Eia nō naʻe, hoʻohana nā ʻepekema ʻikepili i ko lākou ʻike no ka makemakika a me ka ʻikepili e unuhi i ka manaʻo mai ka nui o ka ʻikepili. Ke hilinaʻi nei nā ʻoihana i kēia ʻike no ka hoʻoholo ʻana i hiki ke kōkua iā lākou e loaʻa ka lanakila hoʻokūkū ma luna o kā lākou mau hoa hakakā a ʻike paha i nā manawa hou e hiki ʻole ai iā lākou ke ʻike me ke kōkua ʻole o ka ʻikepili nui.
Ka papa inoa o nā Koleke ʻEpekema ʻIkepili maikaʻi loa ma ka honua
Aia ma lalo kahi papa inoa o nā koleke ʻepekema data 20 maikaʻi loa ma ka honua.
- Ke Kulanui o Kaleponi Berkeley
- Ke Kulanuiʻo Carnegie Mellon
- Ke Kulaʻo Technologyʻo Massachusetts
- Ke Kulanuiʻo Stanford
- Kulanui o Wakinekona
- Cornell University
- Georgia Institute of Technology
- Ke Kulanui ʻo Columbia, New York, NY
- University of Illinois-Urbana-Champaign
- Ke Kulanui o Oxford – United Kingdom
- Nanyang Technological University (NTU) – Singapore
- Imperial College Lādana – United Kingdom
- ETH Zurich (Swiss Federal Institute of Technology) - Switzerland
- ʻO Ecole Polytechnique Federale de Lausanne (EPFL)
- Kulanui o Cambridge
- Kula Nui o ke Kulanui o Singapore (NUS)
- Ke Kulanui Kulanuiʻo London (UCL)
- ke kulanui '
- Ke Kulanui Yale
- Kulanui o Michigan-Ann Arbor.
ʻO nā kula kiʻekiʻe o 20 Data Science ma ka Honua
Ma lalo iho nei kekahi o nā koleke ʻepekema data maikaʻi loa ma ka honua.
1. Ke Kulanui o Kaleponi—Berkeley, CA
Ua helu ʻia ke Kulanui o Kaleponi Berkeley i nā koleke ʻepekema data No.
Ua hoʻokumu ʻia ka mahele o ka ʻepekema helu a me ka ʻikepili a me ka hui ma ke Kulanui o Kaleponi, Berkeley, i Iulai 2019 e hoʻohana i ke kūlana kiʻekiʻe o Berkeley i ka noiʻi a me ka maikaʻi ma waena o nā hoʻopaʻapaʻa e holomua i ka ʻike ʻepekema data, aʻo, a me ka hopena.
Ua kōkua nā kumu a me nā haumāna mai kēlā ʻaoʻao o ka pā kula i ka hoʻokumu ʻana o ka Division of Computing, Data Science, and Society, e hōʻike ana i ke ʻano ʻokiʻoki o ka ʻepekema data a hoʻoponopono hou i ke kulanui noiʻi no ka makahiki kikohoʻe.
Hoʻohui ka ʻōnaehana ikaika o ka Māhele i ka helu helu, nā helu, nā ʻike kanaka, a me ka ʻepekema pilikanaka a kūlohelohe e hoʻokumu i kahi lewa ikaika a hui pū e hoʻoulu i ka noiʻi holomua ma ka ʻoki ʻana o ka ʻepekema a me ka ʻenehana.
2. Kulanui ʻo Carnegie Mellon, Pittsburgh, PA
ʻO ke Kulanui ʻo Carnegie Mellon ka helu 2 mau koleke ʻepekema data e usnews i ka makahiki 2022. He $58,924, 7,073 ka helu haʻahaʻa haʻahaʻa a me ka helu 4.9.
Ua hoʻolālā ʻia ka papahana MS in Data Analytics for Science (MS-DAS) o Carnegie Mellon University no nā haumāna hoihoi e aʻo hou aku e pili ana i nā ʻano like ʻole o ka ʻepekema data.
Hiki i nā haumāna ke hoʻonui i ko lākou ʻike ʻepekema ma ke aʻo ʻana i nā ʻōlelo hoʻolālā hou no nā ʻepekema, ka makemakika a me ka hoʻohālikelike helu helu, nā ʻano helu helu e like me ka computing parallel, computing hana kiʻekiʻe, nā ʻenehana aʻo mīkini, ʻike ʻike ʻike, nā mea hana helu, a me nā pūʻolo polokalamu hou, mahalo. i nā poʻe akamai a me ka ʻenehana o ke Kulanui ʻEpekema Mellon a me ka Pittsburgh Supercomputing Center.
3. Ke Kulaʻo Technologyʻo Massachusetts
Ua helu ʻia ʻo MIT i ka helu 3 ma ka Data Analytics/Science e usnews i ka makahiki 2022. He $58,878 kāna uku haʻawina, 4,361 ka helu haʻahaʻa haʻahaʻa a me ka helu inoa inoa 4.9.
Loaʻa ka Bachelor of Science in Computer Science, Economics, and Data Science ma MIT (Course 6-14). E loaʻa i nā haumāna e hoʻopau ana i ka nui multidisciplinary kahi kōpili o nā mākau i ka ʻoihana waiwai, ka helu helu, a me ka ʻepekema data, e lilo ana i mea waiwai nui ma ka ʻoihana kalepa a me ke kula.
Ke hilinaʻi nui nei nā ʻoihana ʻepekema a me ka lolouila i ka manaʻo pāʻani a me nā ala hoʻohālike makemakika, a me ka hoʻohana ʻana i ka ʻikepili ʻikepili.
ʻO ke aʻo ʻana i nā algorithms, optimization, a me ke aʻo ʻana i nā mīkini he mau laʻana o nā papa ʻepekema kamepiula e hana i ka ʻike hoʻohui (i hoʻohui ʻia me ka econometrics).
Loaʻa nā haʻawina ma nā ʻāpana makemakika like ʻole, e like me ka linear algebra, probability, discrete matematika, a me nā helu helu, ma o nā keʻena he nui.
4. Ke Kulanuiʻo Stanford
ʻO ke Kulanui ʻo Stanford kekahi koleke ʻepekema data kiʻekiʻe e like me ka usnews. Hoʻonoho ʻia ma ke kūlana 4 ma lalo koke o MIT a ma lalo o ke Kulanui o Wakinekona, Seattle, WA. Ua uku ʻo Stanford University i kahi haʻawina o $56169 me kahi helu inoa inoa 4.9.
Ke hoʻokumu ʻia nei ka Data Analytics/Science ma ke Kulanui ʻo Stanford i loko o ke ʻano o ka MS i kēia manawa i ka Statistics.
Hoʻokumu ʻia ka ʻike ʻepekema ʻikepili i ka hoʻomohala ʻana i nā mākau makemakika, helu helu, helu helu, a me ka hoʻonohonoho ʻana i ke kumu i ka hoʻonaʻauao ʻepekema data ma o nā koho maʻamau mai ka ʻepekema data a me nā wahi hoihoi.
5. Kulanui o Wakinekona
Ua helu ʻia ke Kulanui o Wakinekona i nā koleke ʻepekema data No. 5 e usnews i ka makahiki 2022. Loaʻa iā ia kahi haʻawina ma waho o ka mokuʻāina he $39,906 a me ka haʻawina i loko o ka mokuʻāina ʻo $12,076 haʻawina a me ka helu inoa inoa 4.4.
Hāʻawi lākou i kahi papahana kekelē haku ma ka ʻepekema data no nā haumāna e makemake e hoʻomaka a hoʻomohala i kā lākou ʻoihana ma ke kula.
Hiki ke hoʻopau ʻia ka papahana i ka manawa piha a i ʻole hapa manawa.
ʻO kēlā me kēia hapahā o ka hāʻule, hoʻomaka nā papa ma ke kahua kula o ke Kulanui o Wakinekona a ʻākoakoa i nā ahiahi.
E aʻo ʻoe pehea e unuhi ai i nā ʻike koʻikoʻi mai ka ʻikepili nui e hoʻomaikaʻi i ka haʻawina pili i ka ʻoihana.
No ka hoʻokō ʻana i nā pono e piʻi aʻe o ka ʻoihana, ʻaʻole no ka waiwai, nā keʻena aupuni, a me nā hui ʻē aʻe, e loaʻa iā ʻoe ka mākaukau i ka hoʻohālikelike ʻikepili, ka hoʻokele ʻikepili, ke aʻo ʻana i ka mīkini, ka ʻike ʻike ʻikepili, ka ʻenekinia polokalamu, ka hoʻolālā noiʻi, ka ʻikepili ʻikepili, a me ka ʻike mea hoʻohana. ma keia papahana.
6. Cornell University
ʻO Cornell Institution, aia ma Ithaca, New York, he Ivy League kūʻokoʻa a me ke kulanui noiʻi hāʻawi ʻāina.
Ua hoʻokumu ʻia ke Kulanui ma 1865 e Ezra Cornell lāua ʻo Andrew Dickson White me ka pahuhopu o ke aʻo ʻana a me ka hāʻawi ʻana i nā haʻawina ma nā ʻano aʻoaʻo a pau o ka ʻike, mai ka papa mele a hiki i ka ʻepekema, a mai ka theoretical i ka hana.
ʻO ka manaʻo kumu o Cornell, kahi ʻōlelo maʻamau o ka makahiki 1868 mai ka mea hoʻokumu ʻo Ezra Cornell, e hopu i kēia mau manaʻo like ʻole: "E kūkulu wau i kahi kula kahi e loaʻa ai i kēlā me kēia kanaka ke aʻo ʻana i kēlā me kēia haʻawina."
7. Georgia Institute of Technology
ʻO ka Georgia Institute of Technology, i kapa ʻia ʻo Georgia Tech a i ʻole Tech wale nō ma Georgia, he kulanui noiʻi lehulehu a me ka ʻenehana ʻenehana ma Atlanta, Georgia.
He kahua kula ukali ia o ka University System of Georgia, me nā wahi ma Savannah, Georgia, Metz, France, Athlone, Ireland, Shenzhen, Kina, a me Singapore.
8. Ke Kulanui ʻo Columbia, New York, NY
He kulanui noiʻi ʻo Ivy League pilikino ma New York City. ʻO Columbia University, i hoʻokumu ʻia i ka makahiki 1754 ma ke ʻano he King's College ma ke kahua o Trinity Church ma Manhattan, ʻo ia ke kula kahiko loa o ke kula kiʻekiʻe ma New York a ʻo ka ʻelima mau makahiki ma United States.
ʻO ia kekahi o nā koleke colonial ʻeiwa i hoʻokumu ʻia ma mua o ka American Revolution, ʻehiku o ia mau lālā o ka Ivy League. Hoʻonohonoho mau nā puke pai hoʻonaʻauao koʻikoʻi ʻo Columbia ma waena o nā koleke maikaʻi loa ma ka honua.
9. University of Illinois-Urbana-Champaign
Ma nā kūlanakauhale māhoe ʻo Illinois o Champaign a me Urbana, ʻo ka Institution of Illinois Urbana-Champaign he kulanui noiʻi ʻāina ākea.
Ua hana ʻia i ka makahiki 1867 a ʻo ia ke kula nui o ke Kulanui o Illinois. ʻO ke Kulanui o Ilinoi kekahi o nā kula aupuni nui loa o ka ʻāina, me ka 56,000 o nā haumāna lae a me nā haumāna puka puka.
10. Ke Kulanui o Oxford – United Kingdom
Hoʻonohonoho mau ʻia ʻo Oxford ma waena o nā kula ʻelima kiʻekiʻe o ka honua, a i kēia manawa ua helu mua ʻia ma ka honua e like me; ʻO Forbes' World University Rankings; ʻO Times Higher Education World University Rankings.
Ua koho mua ʻia ʻo ia ma ka Times Good University Guide no ʻumikūmākahi mau makahiki, a ua helu mua ʻia ke kula olakino ma ka Times Higher Education (THE) World University Rankings no nā makahiki ʻehiku i hala ma ka "Clinical, Pre-Clinical & Health" papaʻaina.
Ua hoʻonoho ʻo SCImago Institutions Rankings iā ia i ke ono ma waena o nā kulanui a puni ka honua i 2021. A ʻo ia kekahi o nā mea nui loa i ka ʻepekema data.
11. Nanyang Technological University (NTU) – Singapore
He kulanui noiʻi kolepa ʻo Singapore's Nanyang Technological Institution (NTU). ʻO ia ka lua o ke kulanui kūʻokoʻa kahiko loa o ka ʻāina a, e like me ka nui o nā pae honua, ʻo ia kekahi o nā ʻoihana maikaʻi loa o ka honua.
Wahi a ka hapa nui o nā papa inoa, ua hoʻonoho mau ʻia ʻo NTU ma waena o nā keʻena 80 kiʻekiʻe o ka honua, a aia i kēia manawa he 12th i ka QS World University Rankings e like me Iune 2021.
12. Imperial College Lādana – United Kingdom
ʻO Imperial College London, ma ke kānāwai ka Imperial College of Science, Technology and Medicine, he kulanui noiʻi lehulehu ma Lākana.
Ua ulu ʻo ia ma waho o ka ʻike a ke Aliʻi Albert no kahi ʻano moʻomeheu, ʻo ia hoʻi: ka Royal Albert Hall, Victoria & Albert Museum, Natural History Museum, a me kekahi mau Royal Colleges.
I ka makahiki 1907, ua hoʻokumu ʻia ʻo Imperial College e ka palapala hoʻopaʻa aliʻi, e hoʻohui ana i ka Royal College of Science, Royal School of Mines, a me City and Guilds of London Institute.
13. ETH Zurich (Swiss Federal Institute of Technology) - Switzerland
He kulanui noiʻi lehulehu ʻo ETH Zurich ma ke kūlanakauhale ʻo Zürich. Ke nānā nui nei ke kula i ka ʻepekema, ʻenehana, ʻenekinia, a me ka makemakika a ua hoʻokumu ʻia e ke Aupuni Federal Swiss ma 1854 me ke kumu i ʻōlelo ʻia e aʻo ai i nā ʻenekinia a me nā ʻepekema.
He ʻāpana ia o ka Swiss Federal Institutes of Technology Domain, kahi ʻāpana o ka Swiss Federal Department of Economic Affairs, Education, and Research, e like me kona kaikuahine kulanui ʻo EPFL.
14. ʻO Ecole Polytechnique Federale de Lausanne (EPFL)
ʻO EPFL (École polytechnique fédérale de Lausanne) kahi kulanui noiʻi lehulehu o Swiss i hoʻokumu ʻia ma Lausanne. ʻO ka ʻepekema kūlohelohe a me ka ʻenekinia kāna mau mea kūikawā. ʻO ia kekahi o ʻelua Swiss Federal Institutes of Technology, a he ʻekolu kāna mau misionari mua: ka hoʻonaʻauao, noiʻi, a me ka hana hou.
Ua koho ʻia ʻo EPFL i ka 14th kulanui maikaʻi loa ma ka honua ma nā wahi āpau e QS World University Rankings ma 2021, a me 19th kula kiʻekiʻe no ka ʻenekinia a me ka ʻenehana e THE World University Rankings ma 2020.
15. Kulanui o Cambridge
Aia ʻo Cambridge he 31 semi-autonomous constituent koleke a ʻoi aku ma mua o 150 mau keʻena kula, kumu, a me nā hui ʻē aʻe i hoʻonohonoho ʻia i ʻeono kula.
I loko o ke kulanui, ʻo nā kula āpau he mau ʻoihana hoʻokele ponoʻī, kēlā me kēia me kona lālā ponoʻī, hui kūloko, a me nā hana. He ʻāpana kēlā me kēia haumāna o ke kulanui. ʻAʻohe kahua nui no ke kula, a ua hoʻopuehu ʻia kona mau koleke a me nā mea nui a puni ke kūlanakauhale.
16. Kula Nui o ke Kulanui o Singapore (NUS)
Ma Queenstown, Singapore, ʻo ka National Institution of Singapore (NUS) kahi kulanui noiʻi koleke aupuni.
ʻO NUS, i hoʻokumu ʻia i ka makahiki 1905 ma ke ʻano he Straits Settlements a me Federated Malay States Government Medical School, ua manaʻo ʻia ʻo ia kekahi o nā kula hoʻonaʻauao maikaʻi loa a kaulana loa o ka honua, a ma ka ʻāina ʻo Asia-Pacific.
Hāʻawi ia i ka holomua o ka ʻenehana hou a me ka ʻepekema ma o ka hāʻawi ʻana i kahi ala honua i ka hoʻonaʻauao a me ka noiʻi, me ka manaʻo nui i ka ʻike a me nā kuanaʻike ʻĀsia.
Ua helu ʻia ʻo NUS i ka 11th ma ka honua a ʻo ka mua ma Asia i ka QS World University Rankings ma 2022.
17. Ke Kulanui Kulanuiʻo London (UCL)
He kulanui noiʻi lehulehu nui ʻo University College London ma Lākana, United Kingdom.
He lālā ʻo UCL o ke Kulanui Federal o Lākana a ʻo ia ka lua o ke kulanui nui loa o United Kingdom ma ke ʻano o ka helu helu a ʻo ka mea nui loa ma ke ʻano o ke kau inoa postgraduate.
18. ke kulanui '
Ke Kulanui ʻo Princeton, aia ma Princeton, New Jersey, kahi kula ʻimi noiʻi pilikino ʻo Ivy League.
ʻO ke Kulanui ka ʻehā o ke kula kiʻekiʻe o ka hoʻonaʻauao kiʻekiʻe ma ʻAmelika Hui Pū ʻIa, ua hoʻokumu ʻia ma 1746 ma Elizabeth ma ke Kulanui o New Jersey.
ʻO ia kekahi o nā koleke colonial ʻeiwa i hoʻopaʻa ʻia ma mua o ka American Revolution. Hoʻopaʻa pinepine ʻia ma waena o nā kula kiʻekiʻe a mahalo nui ʻia.
19. Ke Kulanui Yale
ʻO Yale Institution kahi kulanui noiʻi ʻo Ivy League i New Haven, Connecticut. ʻO ia ke kolu o nā kula kahiko loa o ka hoʻonaʻauao kiʻekiʻe ma ʻAmelika Hui Pū ʻIa, a ʻo kekahi o nā mea kaulana loa ma ka honua, ua hoʻokumu ʻia ma 1701 ma ke kula ʻo Collegiate.
Ua manaʻo ʻia ke kulanui ʻo ia kekahi o nā kula ʻepekema data nui loa ma ka honua a me ʻAmelika Hui Pū ʻIa.
20. Ke Kulanui o Michigan-Ann Arbor
Ke Kulanui o Michigan, aia ma Ann Arbor, Michigan, he kulanui noiʻi lehulehu. Ua hoʻokumu ʻia ke kula ma 1817 e kahi hana a ka Territory Michigan mua ma ke ʻano he Catholepistemiad, a i ʻole University of Michigania, 20 mau makahiki ma mua o ka lilo ʻana o ka ʻāina i mokuʻāina.
Pinepine ninau ninaninau 'ana i
ʻEhia ka nui o nā mea ʻepekema ʻikepili?
ʻO ka awelika uku uku no kahi ʻepekema data ma US he $117,345 i kēlā me kēia makahiki, e like me Glassdoor. Eia naʻe, ʻokoʻa ka uku e ka hui, me kekahi mau ʻepekema data e loaʻa ana ma mua o $200,000 i kēlā me kēia makahiki.
He aha ka ʻokoʻa ma waena o kahi Data Scientist a me kahi Data Analyst?
Hoʻopili pinepine ʻia nā ʻikepili a me nā ʻepekema data no kekahi i kekahi, akā aia nā ʻokoʻa nui ma waena o lākou. Hoʻohana ka poʻe loiloi ʻikepili i nā mea hana helu no ka nānā ʻana i ka ʻikepili a hōʻike i nā ʻike e kōkua i ke alakaʻi ʻana i nā hoʻoholo ʻoihana, akā hoʻomohala nā ʻepekema data i nā algorithms e mana i kēia mau mea hana a hoʻohana iā lākou e hoʻoponopono i nā pilikia paʻakikī.
He aha ke ʻano o ka degere e pono ai ʻoe e lilo i Data Scientist?
Nui ka poʻe hana e ʻimi nei i nā moho i loaʻa ke kēkelē haku ma ka helu helu, makemakika a ʻepekema kamepiula paha - ʻoiai ʻo kekahi o nā mea noi hoʻokūkū loa e loaʻa iā Ph.D. i loko o kēia mau kahua a me kahi waihona nui o ka ʻike hana.
Pono anei ke aʻo ʻana i ka ʻepekema data?
ʻAe! Hiki i kahi ʻoihana i ka ʻepekema data ke hāʻawi i nā pono intrinsic he nui, e like me ka hoʻoulu ʻana i ka naʻauao a me ka hiki ke hoʻoponopono i nā pilikia paʻakikī me ka noʻonoʻo. Hiki iā ia ke alakaʻi i nā uku kiʻekiʻe a me ka ʻoluʻolu o ka hana.
Paipai pū mākou:
- 30 Kula Estetika ʻoi loa ma ka pūnaewele
- ʻO nā kula ʻenehana lako polokalamu maikaʻi loa ma ka pūnaewele
- ʻO 50+ mau papahana hoʻonaʻauao pūnaewele wikiwiki no nā mākua hana
- 20 Degere maʻalahi loa e kiʻi i ka pūnaewele
- Ke kēkelē laepua maikaʻi loa no ka ʻepekema kamepiula ma ka pūnaewele.
Panina
ʻO ka laina lalo ʻoiai ke holomua nei ka honua, ke ulu wikiwiki nei ka honua o ka ʻepekema data.
Ke holo wikiwiki nei nā kulanui a puni ka honua e hāʻawi i ke kēkelē laepua a me ke kula kiʻekiʻe ma ka ʻepekema ʻikepili, akā he mea hou nō ia, no laila ʻaʻole nui nā wahi e hiki ai ke kiʻi i ke kēkelē ma ia kumuhana.
Eia nō naʻe, manaʻo mākou e kōkua kēia pou iā ʻoe e koho i nā koleke ʻepekema data maikaʻi loa kahi e hiki ai iā ʻoe ke holomua i kāu ʻoihana ma ke ʻano he ʻepekema data.